Regarding primary care, telemedicine refers to phone calls where the patient seeks an appointment with the doctor for remote advice about non-emergency and follow-up medical problems that do not require the doctor to physically see the patient. The use of machine learning and artificial intelligence has steadily grown in the area as well. It is quite common for some preliminary forms of virtual assistance in teleradiology. Still, with the advent of AI, these boundaries are being steadily pushed.
This does not replace face-to-face human consultation, which is indispensable, but complements them. This article covers some AI integration in radiology billing and claims processes.
In what areas can AI help with teleradiology?
Most people think of AI’s applications solely in virtual assistance and performing quick, automated tasks. However, there are other applications too:
Minor consultations
Not all conditions warrant a patient to travel to the hospital. Ai-integrated teleradiology offers a remote option to patients and doctors by doing away with the necessity for a physical visit to get medical advice or treatment for matters that can be solved verbally. This is extremely important for conformations and follow-up when dealing with any quotes regarding previous physical consultations.
Patient monitoring
An added convenience of AI-integrated telehealth consultations is that it makes it easier for health professionals to monitor their patients’ health post-op or conditions or minor changes in their bodies. This can be crucial for laboratory investigations or vital sign monitoring. It makes it easier for doctors to access the radiology records of their patients online and keep track of more accurate diagnoses. It also produces more accurate billings for the different procedures, medicines, etc, which can be accessed by all healthcare practitioners involving the patient in one consolidated space.
Data verification and processing
Experts also wish to see this technology used in customer verifications, sorting medical records, and processing insurance and medical bills. However, the accuracy and safety of this still need some work.
Wrapping Up
That brings us closer to the prospect of AI integration in radiology billing and how they help make claims easier. We’ve seen how machine learning has been implemented in things like data sorting, chat support, etc.
However, with responsible use, it can be used beyond simply responding to emails or summarizing articles. Instead, it has some promising use in the field of medicine, especially healing in the field of remote health and telehealth. However, like all AI applications, this too needs stricter laws on ethics and implementation of its true potential to be unearthed.
